Chemometrics and Intelligent Laboratory Systems最新文献_第4页

Comparative evaluation of lightweight convolutional neural network and vision transformer models for multi-class brain tumor classification using merged large MRI datasets 轻量级卷积神经网络与视觉转换模型在融合大MRI数据集的多类脑肿瘤分类中的比较评价

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2026-02-15 Epub Date: 2025-12-03 DOI: 10.1016/j.chemolab.2025.105609

Omneya Attallah , Ishak Pacal

The accurate classification of brain tumors from MRI scans is important for the timely diagnosis and treatment planning process; however, previous state-of-the-art automatic image classification methods frequently struggle to balance performance with computational cost for clinical applications. In this study, we evaluated twenty lightweight Convolutional Neural Networks (CNN) models and eighteen Vision Transformers (ViT) models for multi-class brain tumor classification using a merged dataset of 17,933 MRI images from 4 categories (glioma, meningioma, pituitary tumors, and healthy brains). The study demonstrated that both groups of architectures can achieve state-of-the-art performance with EfficientNet-b0 (98.36 % accuracy, 4.01 M params) and Tiny-ViT-5M (98.41 % accuracy, 5.07 M params), ranking as the top-performing models for each category. The systematic comparison determined that the proposed lighter models have equivalent or greater performance than established lightweight frameworks, while offering computational advantages, such as MobileViT-xxSmall, which achieved outstanding performance (98.16 % accuracy) with fewer than 1 M parameters. Through benchmarking against fourteen other prior existing frameworks for brain tumor classification, we demonstrated that the top-performing lightweight models of this study maintain stable performances across all evaluation metrics (including precision, recall, and F1 score) and aim to mitigate key weaknesses of prior work, including dataset diversity and model complexity. The findings show very competitive performance across brain tumor classification, highlighting the promise of lightweight architectures to generate accurate and efficient diagnostic support for potential clinical deployment, particularly in low-resource healthcare environments where such efficiencies are vital. Moreover, this work provides useful knowledge that may assist in developing deployable artificial intelligence solutions in neuro-oncology settings.

MRI扫描对脑肿瘤的准确分类对于及时诊断和制定治疗计划至关重要；然而，以前最先进的自动图像分类方法经常在临床应用的性能和计算成本之间取得平衡。在这项研究中，我们使用来自4类（胶质瘤、脑膜瘤、垂体瘤和健康脑）的17,933张MRI图像的合并数据集，评估了20种轻量级卷积神经网络（CNN）模型和18种视觉变形器（ViT）模型的多类别脑肿瘤分类。研究表明，这两组架构都可以在效率网-b0（98.36%的准确率，4.01 M参数）和微型vit - 5m（98.41%的准确率，5.07 M参数）上达到最先进的性能，在每个类别中都是表现最好的模型。系统的比较确定了提出的更轻的模型与现有的轻量化框架具有同等或更高的性能，同时提供计算优势，例如MobileViT-xxSmall，它在少于1 M参数的情况下取得了出色的性能（98.16%的准确率）。通过对其他14个现有的脑肿瘤分类框架进行基准测试，我们证明了本研究中表现最好的轻量级模型在所有评估指标（包括精度、召回率和F1分数）上保持稳定的性能，并旨在缓解先前工作的关键弱点，包括数据集多样性和模型复杂性。研究结果显示，该系统在脑肿瘤分类方面的表现非常有竞争力，突出了轻量级架构为潜在的临床部署提供准确、高效诊断支持的前景，特别是在资源匮乏的医疗环境中，这种效率至关重要。此外，这项工作提供了有用的知识，可能有助于在神经肿瘤学环境中开发可部署的人工智能解决方案。

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引用次数: 0

A multi-source data integration for soybean differentiation through multiblock data analysis using a novel adaptation of ComDim 基于ComDim的多块数据分析的大豆多源数据集成

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2026-02-15 Epub Date: 2025-12-18 DOI: 10.1016/j.chemolab.2025.105621

Rodrigo Canarin de Oliveira , Hector Hernan Hernandez Zarta , Wargner Alonso Moreno Losada , Sebastián Javier Caruso , Hágata Cremasco , Evandro Bona , Douglas N. Rutledge , Diego Galvan

Soybean is a major global commodity. Given its importance, ensuring traceability becomes essential. Genetic, climatic, and soil-related factors influence its chemical composition. Integrating multi-source data using a multiblock analysis represents a powerful approach to differentiating soybeans and monitoring their traceability. This study employed an extension of the ComDim method (also known as Common Components and Specific Weights Analysis, CCSWA) to simultaneously differentiate 20 Brazilian soybean varieties, conventional and transgenic, based on cultivation region and cultivation type. The extension replaced the PCA (Principal Components Analysis) used in classical ComDim by CCA (Common Components Analysis). Forty samples cultivated in Londrina and Ponta Grossa (Paraná, Brazil) were analyzed for their fatty acid, amino acid, isoflavone, and mineral profiles using GC-FID, IEC, HPLC-DAD, and ICP-OES. The CCA-based ComDim results revealed that Common Component 2 (CC2) was primarily responsible for distinguishing the geographical regions of Londrina and Ponta Grossa. The global loadings of CC2 indicated that zinc (Zn), manganese (Mn), oleic acid, arginine, and malonyl genistin were the most influential variables in this component. In contrast, CC3 was associated with differentiating conventional and transgenic cultivars. The global loadings highlighted linoleic acid, oleic acid, α-linolenic acid, malonyl glycitin, malonyl genistin, Fe, Zn, and Mn as the most relevant contributors. The combined CC2 and CC3 plots indicated tendencies toward differentiation of soybean samples by cultivation region and cultivation type. This result highlights the potential of CCA-based ComDim as an effective tool for soybean traceability.

大豆是一种主要的全球商品。鉴于其重要性，确保可追溯性变得至关重要。遗传、气候和与土壤有关的因素影响其化学成分。使用多块分析集成多源数据是区分大豆和监测其可追溯性的有力方法。本研究采用ComDim方法（也称为Common Components and Specific Weights Analysis， CCSWA）的扩展方法，根据种植区域和种植类型同时区分了20个巴西大豆品种，包括常规大豆和转基因大豆。该扩展用CCA（公共成分分析）取代了经典ComDim中使用的PCA（主成分分析）。采用GC-FID、IEC、HPLC-DAD和ICP-OES分析了巴西Londrina和Ponta Grossa （paran）种植的40个样品的脂肪酸、氨基酸、异黄酮和矿物质谱。基于CC2的ComDim结果表明，共同成分2 （Common Component 2, CC2）是区分Londrina和Ponta Grossa地理区域的主要原因。CC2的全球负荷表明，锌（Zn）、锰（Mn）、油酸、精氨酸和丙二醇基genistin是影响该组分的主要变量。相比之下，CC3与常规和转基因品种的分化有关。亚油酸、油酸、α-亚麻酸、丙二醇甘油酯、丙二醇龙胆素、铁、锌和锰是最相关的贡献者。CC2和CC3联合样地显示了大豆样品按栽培区域和栽培类型分化的趋势。这一结果突出了基于ccm的ComDim作为大豆可追溯性的有效工具的潜力。

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引用次数: 0

Chemometrics in Brazil: The early days 巴西的化学计量学：早期阶段

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2026-02-15 Epub Date: 2025-12-15 DOI: 10.1016/j.chemolab.2025.105618

Ieda S. Scarminio , Roy E. Bruns

A short history of the beginning of chemometric activities in Brazil as well as early international interactions are presented. Details of early research efforts on main frame computers, 8-bit microcomputers and the first 16-bit microcomputers are detailed. A very brief discussion of the rapid growth of chemometrics in Brazil as the result of readily available software is given.

介绍了巴西化学计量学活动开始的简短历史以及早期的国际互动。详细介绍了早期对主机计算机、8位微型计算机和第一台16位微型计算机的研究工作。一个非常简短的讨论，化学计量学在巴西的快速增长的结果是现成的软件给出。

引用次数: 0

A hybrid SVM-CPSO-KELM model for the simultaneous detection of methane, ethane, and ethylene via photoacoustic spectroscopy 一种混合SVM-CPSO-KELM模型用于光声光谱同时检测甲烷、乙烷和乙烯

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2026-02-15 Epub Date: 2025-12-17 DOI: 10.1016/j.chemolab.2025.105620

Meixuan Zhao, Pengcheng Gu, Yuwang Han

Photoacoustic spectroscopy (PAS) is a powerful technique for detecting trace gas mixtures, with applications spanning industrial safety, environmental monitoring, and energy systems. However, when it is applied to three crucial indicator gases methane (CH₄), ethane (C₂H₆), and ethylene (C₂H₄), strong spectral overlaps introduce cross-interference that complicates accurate concentration retrieval. To address limitations in conventional chemometric and machine learning approaches—such as poor generalization across concentration ranges and vulnerability to interference—this study proposes a hybrid model integrating Support Vector Machine (SVM) classification with Chaotic Particle Swarm Optimization (CPSO)-enhanced Kernel Extreme Learning Machine (KELM). The workflow includes wavelet-based denoising, feature selection via Competitive Adaptive Reweighted Sampling (CARS), dynamic thresholding by SVM to partition samples into high- and low-concentration regimes, and the eventual regression analysis using KELM. The proposed approach significantly improves detection accuracy across a wide concentration range (0.5–500 ppm). Experimental results show that the SVM-CPSO-KELM model achieves an average prediction error of 5.44 %, with maximum error below 14.37 %.

光声光谱（PAS）是一种检测微量气体混合物的强大技术，其应用范围涵盖工业安全、环境监测和能源系统。然而，当它应用于三种关键的指示气体甲烷（CH4）、乙烷（C2H6）和乙烯（C2H4）时，强烈的光谱重叠会引入交叉干扰，使准确的浓度检索变得复杂。为了解决传统化学计量学和机器学习方法的局限性，例如跨浓度范围的较差泛化和易受干扰，本研究提出了一种将支持向量机（SVM）分类与混沌粒子群优化（CPSO）增强的核极限学习机（KELM）相结合的混合模型。工作流程包括基于小波的去噪，通过竞争自适应重加权采样（CARS）进行特征选择，通过支持向量机进行动态阈值分割，将样本划分为高浓度和低浓度区域，最后使用KELM进行回归分析。所提出的方法显着提高了宽浓度范围（0.5 - 500ppm）的检测精度。实验结果表明，SVM-CPSO-KELM模型的平均预测误差为5.44%，最大误差在14.37%以下。

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引用次数: 0

Integrated monitoring and diagnosis of industrial processes based on causality synergistic and unique decomposition 基于因果、协同和独特分解的工业过程综合监测与诊断

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2026-02-15 Epub Date: 2025-11-30 DOI: 10.1016/j.chemolab.2025.105593

Shijie Zhu , Qi Zhang , Shuai Li , Yang Fu , Dongni Jia , Yigeng Wang

Causality mining plays a crucial role in monitoring complex industrial processes. However, incomplete extraction of quality related information may lead to a reduced monitoring accuracy rate for quality related faults, while uncertain causal relationships during root variable mining can further result in wrong fault diagnosis outcomes. To address these problems, we decompose the causal relationships between variables into synergistic and unique ones and further propose an integrated monitoring and diagnosis approach for industrial processes based on causality synergistic and unique decomposition. Firstly, we use Granger causality to preliminarily identify quality-related features and enhance the extraction of quality related features via the synergistic effect of causal relationships for addressing their complex interdependence. Secondly, due to the synergistic causality among variables between variable groups, it is necessary to capture and model their dynamic characteristics to ensure monitoring accuracy. We extend quality variable fault monitoring to process variables and further achieve integrated monitoring. Finally, we explore causal uniqueness to identify the fault root cause, which is key to achieving precise and rapid diagnosis in complex and uncertain industrial processes. The feasibility and effectiveness of the proposed method were validated in two scenarios: the benchmark Tennessee Eastman (TE) chemical process and an industrial case study of poor iron ore beneficiation.

因果关系挖掘在监测复杂工业过程中起着至关重要的作用。然而，质量相关信息的不完全提取会导致质量相关故障的监测准确率降低，而根变量挖掘过程中因果关系的不确定性会进一步导致错误的故障诊断结果。为了解决这些问题，我们将变量之间的因果关系分解为协同和唯一的因果关系，并进一步提出了基于因果协同和唯一分解的工业过程综合监测与诊断方法。首先，我们利用格兰杰因果关系对质量相关特征进行初步识别，并通过因果关系的协同效应来增强质量相关特征的提取，以解决它们之间复杂的相互依存关系。其次，由于变量组之间的变量之间存在协同因果关系，因此有必要对其动态特性进行捕捉和建模，以保证监测的准确性。将质量变量故障监测扩展到过程变量，进一步实现一体化监测。最后，探讨故障的因果唯一性，识别故障的根本原因，这是在复杂不确定的工业过程中实现精确快速诊断的关键。在田纳西伊士曼（Tennessee Eastman）化工流程和贫铁矿选矿工业案例两种场景下验证了该方法的可行性和有效性。

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引用次数: 0

An enhanced stacking ensemble learning strategy for product quality estimation in complex industrial processes considering multi-timescale data 考虑多时间尺度数据的复杂工业过程产品质量估计的增强叠加集成学习策略

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2026-02-15 Epub Date: 2026-01-09 DOI: 10.1016/j.chemolab.2026.105635

Weihang Sun , Xin Jin , Sen Xie , Rui Wang

The cascaded structure involving multi-process and multi-equipment in complex industrial processes leads to time delays for product quality detection, impacting the optimization and control accuracy of industrial production. To tackle this challenge, an enhanced stacking ensemble learning model for quality estimation in complex industrial processes is developed in this paper. First, features from high-dimensional data are extracted through convolutional neural network (CNN). Subsequently, the base learner is constructed, and the meta-learning model is determined by the optimal weight coefficient selection strategy. In addition, the quality estimation error of the meta-learner is calculated and the error estimation is performed by random forest. Following this, the error compensation is applied to the meta-learner for enhancing the estimation accuracy. Through an actual industrial evaporation case for alumina production, it is demonstrated that, compared with other state-of-the-art estimation models, the estimation model present in this paper not only reduces error level but also significantly improves the estimation reliability, thereby fulfilling the operational requirements of the process industry.

复杂工业过程中涉及多工序、多设备的级联结构导致了产品质量检测的时间延迟，影响了工业生产的优化和控制精度。为了解决这一问题，本文提出了一种用于复杂工业过程质量估计的增强型叠加集成学习模型。首先，通过卷积神经网络（CNN）对高维数据进行特征提取。然后，构建基础学习器，通过最优权系数选择策略确定元学习模型。此外，计算了元学习器的质量估计误差，并用随机森林进行误差估计。在此基础上，对元学习器进行误差补偿，提高估计精度。通过一个氧化铝生产的实际工业蒸发案例表明，与其他最先进的估算模型相比，本文提出的估算模型不仅降低了误差水平，而且显著提高了估算可靠性，满足了过程工业的运行要求。

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引用次数: 0

Automated correction of saturated peaks in untargeted GC-MS: a chemometric approach 非靶向GC-MS中饱和峰的自动校正：一种化学计量方法

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2026-02-15 Epub Date: 2025-11-19 DOI: 10.1016/j.chemolab.2025.105582

Beatriz Quintanilla-Casas, Jesper Løve Hinrich, Paul-Albert Schneide, Rasmus Bro

Gas chromatography (GC) coupled with mass spectrometry (MS) is a widely utilized analytical technique in various research fields, such as food and environmental science to generate untargeted profiles of volatile organic compounds (VOCs). These profiles often contain numerous compounds with diverse functional groups, present in a wide range of concentrations. Despite the multiple advantages of this technique, highly concentrated compounds can lead to problems such as MS detector saturation. This results in a distorted signal and a loss of accurate quantitative information, as the peak area of the saturated ion (m/z) is no longer a reliable estimate of a compound's abundance in a sample. Although detector saturation is a common challenge in chromatography, no robust solution has been developed to effectively address it. Therefore, this study proposes an automated method to detect and correct saturated m/z signals in untargeted GC-MS data, enabling accurate quantification and identification of highly concentrated VOCs. Our approach leverages the fact that saturation in fact manifests as right-censored data in specific masses (m/z), allowing automatic detection and unbiased modelling using tensor decomposition, namely parallel factor analysis 2 (PARAFAC2) models, combined with expectation-maximization imputation to restore saturated m/z values.

气相色谱（GC）联用质谱（MS）是一种广泛应用于食品和环境科学等各个研究领域的分析技术，用于生成挥发性有机化合物（VOCs）的非靶向谱。这些谱通常包含许多具有不同官能团的化合物，其浓度范围很广。尽管该技术具有多种优点，但高浓度的化合物可能导致质谱检测器饱和等问题。由于饱和离子的峰面积（m/z）不再是样品中化合物丰度的可靠估计，这将导致信号失真和准确定量信息的丢失。虽然检测器饱和是色谱中常见的挑战，但还没有开发出强大的解决方案来有效地解决它。因此，本研究提出了一种自动检测和校正非靶向GC-MS数据中饱和m/z信号的方法，实现高浓度VOCs的准确定量和鉴定。我们的方法利用了这样一个事实，即饱和度实际上表现为特定质量（m/z）的右截除数据，允许使用张量分解（即并行因子分析2 （PARAFAC2）模型）进行自动检测和无偏建模，并结合期望最大化输入来恢复饱和的m/z值。

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引用次数: 0

Equivalent and complementary variables screening based on global search mechanism for wavelength optimization in spectral multivariate calibration 基于全局搜索机制的光谱多变量校准波长优化等效互补变量筛选

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2026-02-15 Epub Date: 2025-12-06 DOI: 10.1016/j.chemolab.2025.105613

Honghong Wang, Yan Zhang, Anqi Jia, Ting Wu, Yiping Du

Variable selection is a very effective method to improve performance of a multivariate calibration model when using high-dimensional spectral dataset. The newly proposed screening strategy of equivalent variables (EVs) and complementary variables (CVs) is worthy of attention. In the proposed method a local search mechanism was used to select the EVs, and the selection range was limited to the adjacent area of the basic variables (BVs) selected by a variable selection method, while the variables far from the BVs were not effectively screened. Aiming at overcoming the limitation of this strategy, this study proposed a global search mechanism based on full-spectrum scanning to screen EVs and investigate CVs based on EVs. The CVs selected from the EVs screened by the global search can provide richer and more accurate feature information to improve the performance of the model. Three variable selection algorithms, stability competitive adaptive reweighted sampling (SCARS), competitive adaptive reweighted sampling (CARS) and Monte Carlo and uninformative variable elimination (MC-UVE), were used to screen EVs and CVs. This strategy is applied to three datasets (corn and tablet NIR dataset, UV–visible dataset). In corn dataset, compared with the model established by the combination of CVs and BVs that used the local search mechanism to screen SCARS from the EVs of CARS and MC-UVE, the performance of the model constructed by 30 CVs combined with BVs based on the global search mechanism was significantly improved, RMSEC and RMSEP decreased from 0.0365 and 0.0590 to 0.0305 and 0.0496, respectively. Similarly, the RMSEP of the model prediction results constructed by the CVs of CARS and MC-UVE combined with BVs obtained by the global search decreased from 0.0625 and 0.0505 to 0.0555 and 0.0403, respectively. Similar results were obtained for other datasets.

在使用高维光谱数据集时，变量选择是提高多变量校准模型性能的一种非常有效的方法。新提出的等效变量（ev）和互补变量（cv）的筛选策略值得关注。该方法采用局部搜索机制对电动汽车进行选取，选取范围局限于变量选取法选取的基本变量（bv）的邻近区域，而对远离基本变量的变量没有有效筛选。针对该策略的局限性，本研究提出了一种基于全谱扫描的全局搜索机制来筛选电动汽车，并对基于电动汽车的cv进行研究。从全局搜索筛选的电动汽车中选择的cv可以提供更丰富、更准确的特征信息，从而提高模型的性能。采用稳定性竞争自适应重加权抽样（scar）、竞争自适应重加权抽样（CARS）和蒙特卡罗和无信息变量消除（MC-UVE）三种变量选择算法筛选电动汽车和cv。该策略应用于三个数据集（玉米和片剂近红外数据集，紫外可见数据集）。在玉米数据集中，与使用局部搜索机制从CARS和MC-UVE的ev中筛选scar的cv和bv组合模型相比，基于全局搜索机制构建的30个cv和bv组合模型的性能显著提高，RMSEC和RMSEP分别从0.0365和0.0590降低到0.0305和0.0496。同样，CARS和MC-UVE的cv结合全局搜索得到的bv构建的模型预测结果的RMSEP分别从0.0625和0.0505下降到0.0555和0.0403。其他数据集也得到了类似的结果。

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引用次数: 0

An efficient automated deep spatio-temporal feature learning framework for industrial soft sensing 工业软测量中一种高效的自动化深度时空特征学习框架

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2026-02-15 Epub Date: 2026-01-02 DOI: 10.1016/j.chemolab.2025.105623

Xiaogang Deng , Ziheng Wang , Lumeng Huang , Ping Wang

Deep learning neural networks have been widely adopted for developing quality prediction models in industrial processes. Despite their strong capability of nonlinear intrinsic features, the existing models have some notable drawbacks, such as insufficient capturing of local spatio-temporal features, high computational complexity of model training, difficult determination of deep model structure, and lack of model interpretability. To address these issues, this paper presents an efficient automated deep spatio-temporal feature learning framework for dynamic industrial process soft sensing, named Deep Convolutional Partial Least Squares (DeCPLS). The proposed approach introduces the convolutional Partial Least squares (CPLS) model as a basic feature extraction unit and stacks multiple CPLS layers to construct an efficient deep dynamic feature learning model. A layerwise training mechanism is presented to facilitate the automated determination of model structures and hyperparameters, thereby reducing the computational complexity. Furthermore, a model prediction error explanation mechanism is introduced to analyze prediction outcomes effectively. Compared to classical deep neural networks, the proposed method demonstrates the advantage of efficiently capturing local spatio-temporal features while maintaining acceptable computational complexity. Finally, the superiority of the proposed method is validated through a simulated industrial case study and a real-world industrial application.

深度学习神经网络已被广泛应用于工业过程质量预测模型的开发。现有模型具有较强的非线性固有特征提取能力，但存在对局部时空特征捕获不足、模型训练计算复杂度高、模型深层结构难以确定、模型可解释性不足等问题。为了解决这些问题，本文提出了一种用于动态工业过程软测量的高效自动化深度时空特征学习框架，称为深度卷积偏最小二乘（DeCPLS）。该方法将卷积偏最小二乘（CPLS）模型作为基本特征提取单元，并将多个CPLS层叠加，构建高效的深度动态特征学习模型。提出了一种分层训练机制，便于模型结构和超参数的自动确定，从而降低了计算复杂度。并引入模型预测误差解释机制，对预测结果进行有效分析。与经典深度神经网络相比，该方法在保持可接受的计算复杂度的同时，能够有效地捕获局部时空特征。最后，通过模拟工业案例研究和实际工业应用验证了所提方法的优越性。

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引用次数: 0

Distributed learning of deep residual principal component analysis for large-scale industrial process monitoring 大规模工业过程监测中深度残差主成分分析的分布式学习

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2026-02-15 Epub Date: 2026-01-05 DOI: 10.1016/j.chemolab.2026.105629

Ouguan Xu , Zeyu Yang , Zhiqiang Ge

Distributed principal component analysis (PCA) has been widely used for monitoring large-scale industrial processes in the past years, with lots of improved forms and extension counterparts. This paper introduces a deep residual form of PCA into the distributed modeling framework, in order to improve the monitoring performance for large-scale industrial processes. While deep residual PCA model is developed for feature engineering in each separated block of the process, those augmented features extracted in different blocks are combined together in the second level for construction of an additional deep residual PCA model. By further augmenting the extracted features from different layers of the deep residual model, the final process monitoring scheme can be formulated for large-scale industrial processes. Based on two industrial case studies, the monitoring performance has been improved more than 20 % by the proposed distributed deep learning model, while at the same time the computation burden of the new method has been kept in a low level.

近年来，分布式主成分分析（PCA）被广泛用于大规模工业过程的监测，并有许多改进的形式和扩展的对应形式。为了提高大规模工业过程的监测性能，本文在分布式建模框架中引入了一种深度残差形式的主成分分析。当深度残差PCA模型被开发用于过程中每个分离块的特征工程时，这些在不同块中提取的增强特征在第二级被组合在一起以构建额外的深度残差PCA模型。通过对深度残差模型各层提取的特征进行进一步增强，可以制定针对大规模工业过程的最终过程监控方案。通过两个工业案例研究，所提出的分布式深度学习模型的监测性能提高了20%以上，同时使新方法的计算负担保持在较低的水平。

引用次数: 0